System and method for prescribing fertilizer application rates for spatial distribution of a product

ABSTRACT

A precision agriculture prescription system which provides precision agriculture prescriptions, by estimating fertilizer application rates and providing a prescription for spatial distribution of the selected product over a given field(s) with a goal of achieving an efficient use of budgeted fertilizer product(s). The system utilizes historical and forecast weather data, as well as remote sensing satellite imagery to maximize the grower&#39;s budgeted fertilizer products over his/her fields. This is done by assessing the normalized difference vegetation index (NDVI) from 1-to-many satellite images of the given field(s). The system also leverages regional historical weather data to correlate prior seasons&#39; growth patterns and climate effects, and regional weather forecast data to incorporate predictive climate impacts. A soil mineralization model is applied by the system to prescribe an efficient spatial distribution down to the image pixel level for the selected product(s) over the grower&#39;s field(s), to provide an economic advantage for the grower.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a U.S. 371 Application of International ApplicationPCT/CA2016/050323, filed on Mar. 21, 2016, which claims the benefit ofthe U.S. Provisional Application No. 62/303,856, filed Mar. 4, 2016,both of which are herein incorporated by reference in their entirety.

TECHNICAL FIELD

The following relates to systems and methods for prescribing fertilizerapplication rates for spatial distribution of a product, particularlyfor distributing fertilizer product in an agricultural field.

DESCRIPTION OF THE RELATED ART

Fertilizers are widely used in agricultural applications such as farmingto supply nutrients to the soil in order to yield crops from that soil.Efficient application of fertilizer is often desirable to manage bothmonetary and environmental costs. In some jurisdictions, there may alsobe regulations surrounding fertilizer usage, which makes suchefficiencies a requirement.

To meet these efficiency needs, precision farming and precisionagricultural techniques have been developed which utilize varioustechnologies to vary the rate of fertilizer applied to particular fieldsin particular geographical areas. For example, global positioningsystems (GPS), geographical information systems (GIS), and remotesensing have been utilized to apply fertilizer according to the needs ofindividual soils and soil types in these particular fields.

SUMMARY

The following provides a precision agriculture prescription system whichprovides precision agriculture prescriptions, by estimating fertilizerapplication rates and providing a prescription for spatial distributionof the selected product over a given field(s) with a goal of achievingan efficient use of budgeted fertilizer product(s).

The system can utilize historical and forecast weather data, as well asremote sensing satellite imagery to maximize the grower's budgetedfertilizer products over his/her fields. This can be done by assessing avegetative index such as the normalized difference vegetation index(NDVI) from 1-to-many satellite images of the given field(s). The systemcan also leverage regional historical weather data to correlate priorseasons' growth patterns and climate effects, and regional weatherforecast data to incorporate predictive climate impacts. A soilmineralization model can be applied by the system to prescribe anefficient spatial distribution down to, or lower than, the image pixellevel for the selected product(s) over the grower's field(s), to providean economic advantage for the grower.

In one aspect, there is provided a method of generating prescriptionsfor spatial distribution of fertilizer product, the method comprising:generating a mineralization map using one or more soil images for afield and mineralization data; generating vegetative index values forone or more vegetation images for the field, using weather information;determining a total available fertilizer budget for the field;determining a native soil nutrient supply from the field using themineralization map; and generating a prescription map for the field thatindicates a distribution of the fertilizer product in the totalavailable fertilizer budget using field patterns determined from thevegetative index values, and taking into account the native soilnutrient supply determined from the mineralization map, and the weatherinformation.

In another aspect, there is provided a computer readable mediumcomprising computer executable instructions for performing the method.

In yet another aspect, there is provided a precision agriculture systemcomprising a processor that can reside on a server, such as a cloudbased server, and which executes computer readable instructions tooperate the system according to the above method.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments will now be described by way of example only with referenceto the appended drawings wherein:

FIG. 1 is a block diagram of a precision agriculture system;

FIG. 2 is a block diagram of an example of a system architecture for theprecision agriculture system;

FIG. 3 is a flow diagram illustrating stages performed by a precisionagriculture system in generating a precision agriculture prescription;

FIG. 4 is a flow chart illustrating operations performed in processingimage data for generating a precision agriculture prescription;

FIG. 5 is a screen shot of an example of a user interface for creating anew project and inputting field boundaries;

FIG. 6 is a screen shot of an example of a user interface for inputtingfertilizer parameters;

FIG. 7 is a screen shot of an example of a user interface for editing afield boundary;

FIG. 8 is a screen shot of an example of a user interface foreliminating images;

FIG. 9 is a flow chart illustrating operations performed in generating aprecision agriculture prescription;

FIG. 10 is a screen shot of an example of a user interface for viewingand editing precision agriculture prescriptions;

FIG. 11 is a sequence diagram illustrating computational interactionsmade by the system during an image processing stage;

FIG. 12 is a sequence diagram illustrating computational interactionsmade by the system during a prescription generation stage; and

FIG. 13 is a sequence diagram illustrating computational interactionsmade by the system during a prescription viewing and editing stage.

DETAILED DESCRIPTION

Turning now to the figures, FIG. 1 illustrates an example of aconfiguration for a precision agriculture platform 10 that includes aprecision agriculture prescription system 12 (the “system” 12) forgenerating prescriptions for spatial distribution of a fertilizerproduct. In this example, the system 12 is accessible via a computingdevice 14 over one or more networks 16, including via wired or wirelessconnections. As such, the system 12 can be implemented as a cloud-basedserver or service that is accessible to various users in variouslocations. It can be appreciated that the implementation shown in FIG. 1can also be implemented in a closed system within an organization andthe widely distributed cloud computing based model is used only forillustrative purposes.

The system 12 generates prescriptions for the spatial distribution ofparticular fertilizer products over a given one or more agriculturefields 18. The system 12 utilizes imagery associated with the fields 18,which can be acquired using any available imaging technology such asremote sensing, using one or more image acquisition systems 20. Theimage acquisition 20 shown in FIG. 1 can provide acquired images to thesystem 12 for storage in an imagery database 22 or can upload suchimages directly to the imagery database 22 if granted suitablepermissions and credentials. It can be appreciated that when images areacquired using other sources, not shown in FIG. 1, the computing device14 can also be used to send images to the system 12 or upload themdirectly in a similar manner.

The system 12 includes or otherwise has access to various other datastorage elements as illustrated in FIG. 1. A weather database 24 isutilized to obtain weather-related data (e.g., weather forecast data),and a mineralization database 26 is used to obtain soil mineral data tobe used in generating a prescription for a particular field 18. Thesystem 12 also utilizes field metadata 28 when other data related to thefield 18 is available, and stores the generated prescriptions in aprescription (Rx) data store 30.

As indicated, the system 12 can be implemented in various configurationsand utilizing various technologies, such as a cloud-based deployment.FIG. 2 illustrates an example system architecture that can be used toimplement the system 12. The system 12 in this example architectureincludes an application layer 40 and a platform layer 42. Theapplication layer 40 is an abstraction layer that specifies and isresponsible for protocols and methods used by the computing devices 14in the communication network within the platform 10, to enable anprecision agriculture application to be utilized by a user on such acomputing device 14. The platform layer 42 is an abstraction layer thatspecifies and is responsible for the services utilized by the platform10 to generate prescriptions for particular fields 18. For example, asshown in FIG. 2, the platform layer 42 includes or otherwise providesprescription services 44 for generating the prescriptions, reportingservices 46 for generating prescription reports, and image services 48for enabling the application layer 40 to render images in a userinterface as described in greater detail below, and to perform imageprocessing that is used in prescription generation.

The system 12 also includes a cloud-based infrastructure component 50for enabling a cloud-based deployment, however a server-based deploymentis also possible.

As shown in FIG. 3, at a high level, the system 12 operates to getimages and field data for a particular field 18 in a first stage 60, togenerate a precision agriculture prescription in a second stage 62, andto display, export or otherwise provide spatial outputs 63 (e.g.,shapefiles) and/or reports 65 (which may optionally also include spatialoutputs 63) at a third stage 64. This allows the data obtained in thefirst stage 60 to be output in a spatial output 63 such as a shapefileto illustrate the Rx map, or in a report 65 in the third stage 64, thatis based on a process executed in the second stage 62. The second stage62 can also include a sub-stage 66 or parallel process, that allows auser to edit a prescription based on an intermediate prescription asdiscussed below.

FIG. 4 illustrates further detail regarding the first stage 60. In thefirst stage 60, user authentication occurs at 70, which enables a userto enter credentials (e.g., username and password, biometric inputs,etc.) to access the system 12, the credentials being validated by thesystem 12 at the server or cloud side, and/or by an application at aclient terminal being used to access the system 12. The authenticationperformed at 70 can be configured to utilize various access controlmeasures and permission levels to provide different levels of access todifferent users, if desired by the particular application ororganization utilizing the system 12.

An asynchronous image ingestion process is also executed at 74, and theingested imagery is formatted at 76 for storage in the imagery database22. The imagery is obtained from imagery sources such as the imageacquisition system 20. The images are assessed for cloud cover, imagecorrections when required are applied, geo-corrections,orthorectification, etc.

Once the user has been authenticated, an application is presented to theuser to interact with using the computing device 14. The user entersfield boundaries and attributes of the field at step 78, in anyavailable format, for use in generating the prescription. This step caninclude an option to perform a batch import of multiple field boundarydata and/or field definition shapefiles via an API. The attributesentered vary by the product type, but can include, for example, organicmatter in the field 18, which is based on geography, a nitrogen (N)budget for the field 18, the crop to be planted, whether or notirrigation is used/available, drop years, prescription type, etc.

FIG. 5 illustrates a screen shot 100 of an example of a user interfacefor entering field boundaries, e.g., by selecting a Choose File button102 to upload the shapefiles. As shown in FIG. 5, various search fields104 can be provided to search for growers, divisions, locations, etc. Inthis example, grower details are provided as a search result 106. Animage viewing pane 108 is also provided for viewing the shapefiles. FIG.6 illustrates a screen shot 150 of an example of a user interface forentering the input parameters used to generate a prescription. In thisexample a Nitrogen prescription type option 152 has been selected, whichdisplays the nitrogen inputs in a viewing pane 154. A series of inputoptions 156 are also provided to enable the inputs to be entered, forexample, crop type, % organic matter, N budget, irrigation information,guaranteed analysis to convert a rate from weight of a particularcomponent (N/K/P/S) to a rate of a specified product (having apercentage of the component of interest), override option, minimumnitrogen, maximum nitrogen, solid information related to the producttype, and density.

In addition to inputting field boundaries and parameters in step 78, theuser can also optionally manually draw a field boundary at step 80. Thisfunctionality provides a method for creating a field by drawing apolygon over a background image to define the field boundary. Theproduct type and attributes mentioned above would also be associatedwith the manually drawn field. FIG. 7 illustrates a screen shot 200 ofan example of a user interface for drawing and/or editing a fieldboundary 202. The field boundary 202 is displayed over a field image 204and can be interacted with in order to edit the boundary 202 to bettercorrespond to the field being fertilized. Various administrative options206 are also shown, which can include grower details, access to recentreports, and a list of recently created/accessed projects.

The field boundaries input at 78 and 80 are then validated at step 82.The system 12 validates the correctness of the field boundaries andparameters that have been input to ensure that this data is not out ofrange or otherwise detectably incorrect. The valid data is then storedas field metadata 28. The system 12 then retrieves the field metadata 28and the associated images from the imagery database 22 at step 84. Thegeographic location of the field 18 influences the image dates and imageselection. For example, valid crop data ranges vary for geographicregions in say, Canada or the northern United States, versus regions inthe southern United States or Mexico, since growing seasons can bedrastically different.

When the images are retrieved, they are reviewed to eliminate images at86. For example, some images from the set of images associated with thefield 18 may not be usable due to cloud cover, snow cover, etc. A reportof the selected images can also be generated during or upon completionof step 86. FIG. 8 illustrates a screen shot 250 of an example of a userinterface for viewing and eliminating images. In the example shown inFIG. 8, a Landsat image 252 is displayed beside the calculated NDVIimage 254.

FIG. 9 illustrates operations performed by the system 12 in generating aprescription in stage 62, which can be exported, printed, displayed orotherwise provided as a report in stage 64. The prescription generationprocess starts at step 300 and begins by splitting the images fordifferent processing, namely processing for soils and processing forthose have a vegetative index associated therewith, such as theNormalized Difference Vegetation Index (NDVI) used herein asillustrative only. The image set is split into a sub-set of soil imagesat step 302 and a sub-set of NDVI images at 304 such that, for thecollection of images available for that field, the system 12 determineswhich images are useful for soil calculations, and which are useful forNDVI calculations. For example, in a particular geographical region, theimagery used for soil content calculations are within the 20 April to 7May date range when the images would capture tilled soil. In the sameregion, imagery used for NDVI calculations may be within the 21 June to1 September range representing when a crop is likely to be shown in theimages (i.e. having vegetation cover).

The soil images in 302 are used to create a mineralization map at 306.The mineralization map values are derived from the moisture determinedfrom the soil content and a soil mineralization model, such as:N _(total)=[N _(tert) +N _(mn)],

wherein N_(tert) corresponds to the grower's fertilizer budget (for aparticular field), which is typically a user input; and wherein N_(mn)is derived at a pixel level using a soil moisture map and a Net SoilNitrogen Mineralization Model from the mineralization database 26.

Calculating the N_(mn) for each pixel can be performed as follows:

a) Eliminate pixels that include field with clouds of, for example, 5%or greater using band 1/thermal.

b) Eliminate images with pixel fill values which are images where nodata from the satellite sensor is available.

c) Calculate median moisture ranking values per pixel.

d) Map to unit weight (e.g. pounds) of N from mineralization using theNet Soil Nitrogen Mineralization Model. Mapping using the Model caninclude the following parameters:

i) Organic matter for the field (user input or from a geography-baseddatabase);

ii) Plant nitrogen uptake efficiency factor;

iii) Time;

iv) Plateau factor for the area (e.g., in the United States); and

v) Irrigated fields (user input).

The NDVI images obtained at step 304 are processed at step 308 to weightthe image data. The image data is weighted using an algorithm that takesinto account historical weather data and long range/seasonal forecastsobtained from the weather database 24. The weighting takes into accountthe number of images available for the given year, and analogous weatherconditions, to provide the user with a weighted data set from which tocalculate the weighted NDVI at step 310.

A distribution of N can now be determined at step 312, to generate abaseline N-budget from NDVI pixel content, and soil mineralization data.The NDVI calculation can be performed as follows, using the fieldboundaries and the appropriate input images within the appropriate daterange (e.g., June 21 to September 1):

a) Eliminate images with fill values.

b) Apply default weights to images based on day, year, number of imagesin a year, weather (Wx weighting based on precipitation—If the upcomingyear has a higher than normal expected precipitation, weight the imagesfrom years with above average precipitation higher).

c) Calculate the weighted mean for each pixel based on NDVI values fromthe stack. The images can also be further processed to improve theaesthetics of the output, if so desired.

The NDVI values at each pixel can be clipped to eliminate high or lowvalues based on a ranking in the field. For example, an upper limit ofthe 98^(th) percentile and a lower limit of the 2^(nd) percentile can beused. The NDVI values are then transformed into an N interval dependingon the N-budget or user override inputs.

An intermediate Rx map is then generated at step 314. This can be doneusing the transformed NDVI values by taking the total N needed in a unitof the field and subtracting the N from mineralization. That is, the Nrequired for one unit of the field is computed by distributing the userinput total N (i.e. the user-supplied N budget available) using fieldpatterns observed by computing the NDVI values, and correcting for Nfrom mineralization.

As shown in FIG. 9, the intermediate Rx map can be optionally edited bythe user at step 316 to modify the Rx rates on the output from step 314.The editing can include, for example, entering checkstrips, fieldskirts, smoothing, and field merge operations. A checkstrip refers to acontrol sample area of the field where a standard amount of fertilizeris applied to provide a comparison sample against the rest of the field.A field skirt refers to an area of the field to which a user definedrate of fertilizer is to be applied. Smoothing refers to a setting tothrottle/ramp up/down the application rate that prevents the applicationmachinery from changing rates too quickly. Field merge operations referto operations that combine multiple fields to generate a singleprescription.

At step 318, a final Rx is generated by taking into account all of thereceived user inputs and the calculations described above. The output isprovided in rates/classes, and different rates/classes can be providedas options for the output. Different areas of the field (classes) havedifferent product rates prescribed, which makes the Rx a variable rateRx. Depending on how much fertilizer is to be put on a given area of thefield, a rate is selected to achieve that amount, according to theequipment available and how many rates it utilizes. The total N isconverted to a rate based on the product to be used and the final Rx mapis grouped into a rate map with 100 classes. The per-class N rate isthen converted to a product rate, for example, lbs/acre for solidfertilizer, or gallons/acre for liquid fertilizer.

FIG. 10 illustrates a screen shot 450 of an example of a user interfacefor viewing and editing an Rx. A color-coded image 452 can be displayedto illustrate the various N classes/rates to be applied to the regionsof the field according to the variable rate Rx. The N input attributescan also be assigned using an Assign button 454, which enables auser-driven editing of the Rx rates. It can be appreciated that the Rxcan be edited by away of a general defined override/substitute of an Rxrate.

The final Rx is then stored in the Rx data store 30. Moreover, as shownin FIG. 3, one or more reports can be provided at stage 64. The spatialoutputs 63 and/or reports 65 can be formatted in any suitable manner andthe shapefiles are generated for the generated Rx, which can be outputas is, embedded in the reports 65 or provided as data that isreconstructed at a recipient location. Supporting data for the farmer,the field, etc. can also be included in such reports.

Turning now to FIGS. 11 to 13, sequence diagrams are shown for an imageprocessing stage (FIG. 11), a prescription generation stage (FIG. 12),and a prescription viewing and editing stage (FIG. 13) that isimplemented by the system 10 using the example architecture shown inFIG. 2.

The sequence diagrams illustrate various data communications, datastorage, and other interactions between several components in thesystem. However, these communications and interactions are purelyillustrative and can be performed in other ways without departing fromthe principles discussed herein. A user 500 in this example interactswith an app 502. The app 502 interacts with several system components,which also are able to interact with each other as discussed below.These system components include field services 504, a Rx manager 506, arequest manager 508, a scene service 510, a scene recommender 512 andfield metadata 514 (e.g. stored in the field metadata data store 28).

In this example, the user 500 accesses the app 502 by opening an Rxdisplay to initialize the app 502. This causes the app 502 to render alanding page which is displayed for the user 500 on their device. Theuser 500 may then manually draw the fields, when applicable. This issuesan edit field command that is passed by the app 502 to the fieldservices 504 to enable the field services 504 to save the fieldselection and synchronize the field metadata 28, by issuing a syncfields command to the field metadata 514 entity.

The user 500 can also view the fields and search for particular fieldsusing the app 502. This is done by sending a search command to the fieldservices 504, which accesses the field metadata 514. The field meta data514 returns field information based on the search and the field services504 returns the appropriate fields to the app 502. The app 502 rendersthe field information and displays this for the user 500.

Field selections and input parameters can be entered by the user 500,which generates a request that is passed along by the app 502 to therequest manager 508. The request manager 508 determines an Rx status andlogs the request. The user inputs are also validated by the requestmanager 508. It can be appreciated that as illustrated in FIG. 11, if anerror is encountered, the user 500 is notified by the request manager508. The request is saved and a request ID is returned by the requestmanager 508 to the user 500. The user 500 is then able to view theprogress of the request by updating the application to show the userwhat is being processed, which is shown in FIG. 12.

As illustrated in FIG. 12, the request manager 508 performs a get scenesoperation to gather the appropriate images, and creates an Rx for the Rxmanager 506. The Rx manager 506 returns an Rx ID for the particular Rxbeing generated. The Rx manger 506 proceeds to calculate the NDVI, e.g.,as described above. The Rx manager 506 also gets soil mineral data,calculates soil moisture, calculates weather weighting, gets Wx data,generates an image report and generates the intermediate Rx. The requestmanager 508 receives a complete notification (i.e. a notification thatthe process is complete) and the Rx manager 506 saves the Rx data in theRx data store 30, and saves an image report in the imagery database 22.

As shown in FIG. 13, the user 500 selects an option to export a reportand the Rx manager 506 communicates with the app 502 to display thereport in order to allow the user 500 to view the Rx. The user 500 canalso perform an image selection, which initiates a select command viathe app 500, which is passed to the scene service 510. The scene service510 saves the image selection and can repeat the operations in FIG. 12if required, to re-calculate the NDVI and the Rx. The Rx is thendisplayed for the user 500 via the app 502, and the user 500 caninitiate an edit Rx operation. This causes an edited Rx to be displayedusing the app 502. The user 500 is then able to export a report and/orspatial output corresponding to the edited Rx.

For simplicity and clarity of illustration, where consideredappropriate, reference numerals may be repeated among the figures toindicate corresponding or analogous elements. In addition, numerousspecific details are set forth in order to provide a thoroughunderstanding of the examples described herein. However, it will beunderstood by those of ordinary skill in the art that the examplesdescribed herein may be practiced without these specific details. Inother instances, well-known methods, procedures and components have notbeen described in detail so as not to obscure the examples describedherein. Also, the description is not to be considered as limiting thescope of the examples described herein.

It will be appreciated that the examples and corresponding diagrams usedherein are for illustrative purposes only. Different configurations andterminology can be used without departing from the principles expressedherein. For instance, components and modules can be added, deleted,modified, or arranged with differing connections without departing fromthese principles.

It will also be appreciated that any module or component exemplifiedherein that executes instructions may include or otherwise have accessto computer readable media such as storage media, computer storagemedia, or data storage devices (removable and/or non-removable) such as,for example, magnetic disks, optical disks, or tape. Computer storagemedia may include volatile and non-volatile, removable and non-removablemedia implemented in any method or technology for storage ofinformation, such as computer readable instructions, data structures,program modules, or other data. Examples of computer storage mediainclude RAM, ROM, EEPROM, flash memory or other memory technology,CD-ROM, digital versatile disks (DVD) or other optical storage, magneticcassettes, magnetic tape, magnetic disk storage or other magneticstorage devices, or any other medium which can be used to store thedesired information and which can be accessed by an application, module,or both. Any such computer storage media may be part of the precisionagriculture platform 10, any component of or related to the platform 10,etc., or accessible or connectable thereto. Any application or moduleherein described may be implemented using computer readable/executableinstructions that may be stored or otherwise held by such computerreadable media.

The steps or operations in the flow charts and diagrams described hereinare just for example. There may be many variations to these steps oroperations without departing from the principles discussed above. Forinstance, the steps may be performed in a differing order, or steps maybe added, deleted, or modified.

Although the above principles have been described with reference tocertain specific examples, various modifications thereof will beapparent to those skilled in the art as outlined in the appended claims.

The invention claimed is:
 1. A method of generating prescriptions forspatial distribution of fertilizer product, the method comprising:generating a mineralization map using one or more soil images for afield and mineralization data; wherein the mineralization map isgenerated by determining a pixel level soil nutrient supply value usinga soil moisture map and a mineralization model; and wherein the pixellevel soil nutrient value is calculated by: eliminating pixels with apredetermined amount of cloud coverage; eliminating images having fillvalues; calculating moisture ranking values per pixel; and mapping themoisture ranking values to a unit of weight; generating vegetative indexvalues for one or more vegetation images for the field, using weatherinformation; determining a total available fertilizer budget for thefield; determining a native soil nutrient supply from the field usingthe mineralization map; and generating a prescription map for the fieldthat indicates a distribution of the fertilizer product in the totalavailable fertilizer budget using field patterns determined from thevegetative index values, and taking into account the native soilnutrient supply determined from the mineralization map, and the weatherinformation.
 2. The method of claim 1, wherein the prescription map isgenerated by: generating an intermediate prescription map; editing theintermediate prescription map according to at least one user input; andgenerating a final prescription map based on the editing.
 3. The methodof claim 1, wherein the weather information is used to apply weights tothe image data to calculate weighted vegetative index values.
 4. Themethod of claim 1, wherein the fertilizer budget corresponds to anitrogen budget.
 5. The method of claim 1, further comprising outputtingthe prescription map.
 6. The method of claim 5, wherein the prescriptionmap is displayed in a user interface.
 7. The method of claim 5, whereinthe prescription map is provided in one or more reports and/or as aspatial output.
 8. The method of claim 1, wherein the prescription mapprovides an image pixel level value indicative of a rate to apply thefertilizer product.
 9. The method of claim 1, wherein the prescriptionmap provides one of a predetermined number of rates to apply to each ofa plurality of portions of the field.
 10. The method of claim 1, whereinthe vegetative index values are determined according to field boundariesand input images within a predetermined date range.
 11. The method ofclaim 10, wherein the vegetative index values are determined, at leastin part by: eliminating images with fill values; applying weights basedon the weather information; and calculating a weighted mean for eachpixel based on the vegetative index values.
 12. The method of claim 11,further comprising clipping the vegetative index values at each pixel toeliminate high and/or low values based on a ranking in the field. 13.The method of claim 12, further comprising transforming the clippedvalues into an interval based on the fertilizer budget.
 14. The methodof claim 1, wherein the prescription map is generated by subtracting thenative soil nutrient supply available from a portion of the field froman amount of fertilizer prescribed for that portion of the field, andaveraging the result according to the total available fertilizer budget.15. The method of claim 1, further comprising separating a set of imagesinto a first sub-set comprising the one or more soil images, and asecond sub-set comprising the one or more vegetation images.
 16. Themethod of claim 1, further comprising enabling field boundaries andparameters to be input.
 17. The method of claim 16, further comprisingenabling field boundaries to be manually drawn.
 18. The method of claim15, further comprising validating field information prior to obtainingthe one or more soil images and the one or more vegetation images. 19.The method of claim 1, further comprising formatting newly obtainedimagery and storing in an imagery database.
 20. The method of claim 1,further comprising enabling at least one image to be eliminated based onone or more criteria.
 21. The method of claim 1, wherein the vegetativeindex values correspond to normalized difference vegetation index (NDVI)values.
 22. A computer readable storage medium comprising computerexecutable instructions for generating prescriptions for spatialdistribution of fertilizer product, the computer executable instructionscomprising instructions for performing the method of claim
 1. 23. Asystem for generating prescriptions for spatial distribution offertilizer product, the system comprising a processor and memory, thememory comprising computer executable instructions for causing theprocessor to perform the method of claim
 1. 24. The system of claim 23,comprising a cloud-based precision agriculture architecture comprisingone or more databases for storing imagery, mineralization information,weather information, and field metadata; the architecture configured tobe accessible to a device for performing the method.